where \(P\)  is a target distribution of clusters, and \(KL(P||Q)\)  is interpreted as the amount of information lost when Q is used to approximate P. The clustering loss aims to minimize the \(KL\)  divergence so that our estimated cluster distribution converges to the target cluster distribution. We follow [1]} to define the target distribution as:
\(p_{ik}=\frac{q_{ik}^2/\sum _{i=1}^N q_{ik}}{\sum _{k=1}^K (q_{ik}^2/\sum _{i=1}^N q_{ik})}\) 
